A Tutorial on the Planning of Experiments

Freeman, Laura J.; Ryan, Anne G.; Kensler, Jennifer L. K.; Dickinson, Rebecca M.; Vining, G. Geoffrey

doi:10.1080/08982112.2013.817013

Cited by 41 publications

(24 citation statements)

References 15 publications

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“…For the details on the pre‐experimental planning (e.g. problem define, response value define or factors, level and range choose), readers could see the references .…”

Section: The Idea Methodsmentioning

confidence: 99%

Iterative Designed Experiment Analysis (IDEA)

Zhou

Goh

2016

Quality & Reliability Eng

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In the traditional use of design of experiments (DOE), the analysis of a designed experiment proceeds only when the designed plan has been fully carried out and the response values are all available, a standard practice that could be inefficient in time and cost and inadequate when quick indicative results are needed. A procedure referred to as 'iterative designed experiment analysis (IDEA)' is proposed for suggesting results progressively and giving early indications in situations where response values are available only gradually. The procedure is expected to be useful in the early stages of experimental discovery, especially in factor screening situation. Two case studies illustrate and suggest the potential of the proposed method. The IDEA MethodExperimentation design is known to be a learning process that involves iterative cycles of investigations 2 . IDEA is rooted in the same 'learning' paradigm, and the procedure is sketched by the flowchart shown in Figure 1. We here elaborate on some of the key points.

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“…For the details on the pre‐experimental planning (e.g. problem define, response value define or factors, level and range choose), readers could see the references .…”

Section: The Idea Methodsmentioning

confidence: 99%

Iterative Designed Experiment Analysis (IDEA)

Zhou

Goh

2016

Quality & Reliability Eng

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“…Each one of these issues has been discussed in the literature, namely, in the classical textbooks by Box et al and Myers et al , where RSM is comprehensively exposed. Authors who discussed non‐statistical issues and presented guidelines for designing and conducting experiments are Simpson et al , Freeman et al , Tanco et al , and Costa et al …”

Section: Multiresponse Optimization In the Response Surface Methodolomentioning

confidence: 99%

Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Costa

Lourenço

2015

Quality & Reliability Eng

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This paper explores the benefits of Gaussian process model as an alternative modeling technique for problems developed in the Response Surface Methodology framework. Three case studies with different type and number of responses were investigated, and the compromise solutions obtained with three modeling techniques were evaluated. Results provide evidences of the Gaussian process model usefulness for stochastic responses, namely, when responses are correlated.

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“…ey compare fixed and random group effect models and showed that the latter provides more accurate estimates and predictions. Freeman and Vining [17] and Freeman et al [18] provided analysis techniques for reliability data from a designed experiment containing subsampling, in which a NLMM is used to incorporate the random effect. Kensler et al [19] extended the NLMM method further to reflect experiments containing subsampling and random blocks simultaneously.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Analysis of Step-Stress Accelerated Life Test with Random Group Effects under Weibull Distribution

Juan

2020

Mathematical Problems in Engineering

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Step-stress accelerating life test (SSALT), aiming to predict the failure behavior under use condition by the data collected from elevated test setting, is implemented to specimen with time-varying stress levels. Typical testing protocols in SSALT, such as subsampling, cannot guarantee complete randomization and thus result in correlated observations among groups. To consider the random effects from the group-to-group variation, we build a nonlinear mixed effect model (NLMM) with the assumption of Weibull distribution for life time data analysis from SSALT. Both maximum likelihood estimation (MLE) and Bayesian inference are introduced for the estimation and prediction of model parameters as well as other statistics of interest. Gauss–Hermite (G-H) quadrature is used to obtain an accurate approximation of the likelihood for observations, and different priors of model parameters are applied in Bayesian analysis. A comprehensive simulation study and an analysis for a real data set are conducted to justify the proposed method, suggesting that the incorporation of the random effect from the underlying experimental routine ensures a more solid and practical conclusion when the heterogeneous group effect is statistically significant.

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A Tutorial on the Planning of Experiments

Cited by 41 publications

References 15 publications

Iterative Designed Experiment Analysis (IDEA)

Iterative Designed Experiment Analysis (IDEA)

Gaussian Process Model – An Exploratory Study in the Response Surface Methodology

Data Analysis of Step-Stress Accelerated Life Test with Random Group Effects under Weibull Distribution

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